In this project we want to demonstrate how machine learning (clustering) and spatial visualization can inform crime prevention authorities in major cities. For our spatial visualization, we use the Leaflet-package. Analyzing factors that may affect unlawful behavior is beyond the scope of this project[1].
The following R packages are required for this project:
library(readr); library(dplyr); library(DT); library(ggrepel); library(leaflet)The data we use for this project was downloaded from the Atlanta Police Department (PD) website (www.atlantapd.org). The data represent crimes in six policing zones (precincts). The data was contained in ten ZIP files, one for each year from 2009 to 2018 (partial). We joined the comma separated sets (CSV) sets together in Excel and the resulting set is atlanta_crime_10yr.xls, with 619663 records and thirteen variables, name as follows (I renamed some of them):
Here we load the data using readr and read_csv(). For reproducibility, we include the source of the data.
# path <- "https://github.com/stricje1/VIT_University/blob/master/Crime_Analysis_Mapping/data/atlanta_crime_4yr.zip"
path <- "c:\\Users\\jeff\\Documents\\VIT_University\\data\\atlanta_crime_4yr.csv"
df <- read_csv(path)Here, we use the DT package to generate a datatable of crime incidences by category, description, etc. [2]
df_sub <- df[1:100,] # display the first 100 rows
df_sub$Time <- as.character(df_sub$Time)
datatable(df_sub, options = list(pageLength = 5,scrollX='400px'))Here we apply sprintf(), a wrapper for the C function sprintf, that returns a character vector containing a formatted combination of text and variable values.
sprintf("Number of Rows in Dataframe: %s", format(nrow(df),big.mark = ","))## [1] "Number of Rows in Dataframe: 515,807"The All-Caps text is difficult to read, so we coerce the text in the appropriate columns into proper case. First, we define a function proper_case to perform the task. [2]
proper_case <- function(x) {
return (gsub("\\b([A-Z])([A-Z]+)", "\\U\\1\\L\\2" , x, perl=TRUE))
}Now, we apply proper_case() to the dataframe headings and construct the datatable.
df <- df %>% mutate(Category = proper_case(Category),
Descript = proper_case(Descript),
PdDistrict = proper_case(PdDistrict),
Resolution = proper_case(Resolution),
Time = as.character(Time))
df_sub <- df[1:100,] # display the first 100 rows
datatable(df_sub, options = list(pageLength = 5,scrollX='400px'))In this section, we use the leaflet() function. It creates a Leaflet map widget using htmlwidgets. The widget can be rendered on HTML pages generated from R Markdown. In addition to matrices and data frames, leaflet supports spatial objects from the sp package and spatial data frames from the sf package. We create a Leaflet map with these basic steps: First, create a map widget by calling leaflet(). Next, we add layers (i.e., features) to the map by using layer functions (e.g. addTiles, addMarkers, addPolygons) to modify the map widget. Then you keep adding layers or stop when satified with the result.
We will add a tile layer from a known map provider, using the leaflet function addProviderTiles(). A list of providers can be found at http://leaflet-extras.github.io/leaflet-providers/preview/. We will also add graphics elements and layers to the map widget with addCondtroll(addTiles). We use markers to call out points on the map. Marker locations are expressed in latitude/longitude coordinates, and can either appear as icons or as circles. When there are a large number of markers on a map as in our case with crimes, we can cluster them together.
Now, we define crime incident locations on the map employing leaflet, which we use for our popups.
data <- df[1:10000,] # display the first 10,000 rows
data$popup <- paste("<b>Incident #: </b>", data$IncidntNum, "<br>", "<b>Category: </b>", data$Category,
"<br>", "<b>Description: </b>", data$Descript,
"<br>", "<b>Day of week: </b>", data$DayOfWeek,
"<br>", "<b>Date: </b>", data$Date,
"<br>", "<b>Time: </b>", data$Time,
"<br>", "<b>PD district: </b>", data$PdDistrict,
"<br>", "<b>Resolution: </b>", data$Resolution,
"<br>", "<b>Address: </b>", data$Address,
"<br>", "<b>Longitude: </b>", data$X,
"<br>", "<b>Latitude: </b>", data$Y)In this manner, we can click icons on the map to show incident details. We need to set up some generate some parameters that we concatenate or “paste” together to form these incident descriptions. For example, the concatenated strings pdata$popup, provides the content of the second incident as shown here:
data$popup[1]## [1] "<b>Incident #: </b> 150098210 <br> <b>Category: </b> Robbery <br> <b>Description: </b> Robbery, Bodily Force <br> <b>Day of week: </b> Sunday <br> <b>Date: </b> 2/1/2015 <br> <b>Time: </b> 15:45:00 <br> <b>PD district: </b> Zone10 <br> <b>Resolution: </b> None <br> <b>Address: </b> 300 Block of LEAVENWORTH ST <br> <b>Longitude: </b> -84.35398903 <br> <b>Latitude: </b> 33.80414172"The outcome of our Leaflet map shows clusters of crime in the city limits. Clicking on a cluster like the one with 876 crimes in East Atlanta (along I-20) results in smaller clusters. These can also be “zoomed-in” to reveal smaller clusters and eventually individual incidences. These individual crimes have pop-up markers to show the crime details. The +/- toggles zoom in and out as well.
leaflet(data, width = "100%") %>% addTiles() %>%
addTiles(group = "OSM (default)") %>%
addProviderTiles(provider = "Esri.WorldStreetMap",group = "World StreetMap") %>%
addProviderTiles(provider = "Esri.WorldImagery",group = "World Imagery") %>%
# addProviderTiles(provider = "NASAGIBS.ViirsEarthAtNight2012",group = "Nighttime Imagery") %>%
addMarkers(lng = ~X, lat = ~Y, popup = data$popup, clusterOptions = markerClusterOptions()) %>%
addLayersControl(
baseGroups = c("OSM (default)","World StreetMap", "World Imagery"),
options = layersControlOptions(collapsed = FALSE)
)Major cities throughout the United States perform this kind of analysis, and I introduced it to Chennai, India in 2018, while teaching data analytics at the Vellore Institute of Technology.
[1] Strickland, J. (2019). Predictive Crime Analysis using R. Lulu.com. ISBN 978-0-359-43159-5.
[2] Strickland, J. (2017). Data Science Applications using R. Lulu.com. ISBN 978-0-359-81042-0.